Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.

Abstract

Neural system injuries remain a major clinical challenge because of the limited regenerative capacity of neural tissues and the formation of inhibitory post-injury microenvironments. Self-assembling peptide hydrogels (SAPHs) have emerged as a highly biomimetic class of materials for neural repair, owing to their nanofibrous architecture, excellent biocompatibility, injectability, and sequence-programmable properties. However, traditional SAPH design largely depends on empirical screening and mechanistic intuition, which limits efficient exploration of the vast peptide sequence space and hinders prediction of the complex relationships among molecular design, supramolecular assembly, material properties, and regenerative outcomes. This review discusses how established SAPH design principles can be reorganized into an AI-assisted design framework for neural regeneration. Within this framework, peptide sequence, assembly behavior, hydrogel performance, and biological responses are integrated as computable and experimentally verifiable design variables. The review summarizes the evolution of SAPH design, outlines AI-assisted workflows covering data construction, feature encoding, predictive modeling, generative design, optimization, and validation, and discusses their potential applications in immunomodulation, vascular reconstruction, neuronal support, Schwann cell or glial regulation, and functional recovery. Key challenges related to data quality, reproducibility, safety, manufacturability, and translation are also considered. Overall, this review provides a design-oriented perspective for advancing SAPHs from empirically optimized materials toward more predictable, iterative, and translationally relevant regenerative platforms.